Analyzing economic trends in Nepal

# Import packages
#install.packages("corrplot")
library(dplyr)
library(data.table)
library(ggplot2)
library(pastecs)

Attaching package: <91>pastecs<92>

The following objects are masked from <91>package:data.table<92>:

    first, last

The following objects are masked from <91>package:dplyr<92>:

    first, last

library(corrplot)
#library(ggthemes) # For appearance of plot like theme in ggplot2

# Setting environment
# remove(list=ls())
# setwd("C:\\Users\\sunil\\Downloads\\College\\DAV\\Project")
# make evironment not to change large number to exponential
options(scipen = 999)

# Import dataset
nepal_dt <- read.csv("Source Dataset-API_NPL_DS2.csv", skip=4, header=TRUE, stringsAsFactors = FALSE)
meta_country <- read.csv("MetaData_Country.csv", header=TRUE, stringsAsFactors = FALSE)
meta_indictr <- read.csv("MetaData_Indicator.csv", header=TRUE, stringsAsFactors = FALSE)
nepal_dt

meta_country

meta_indictr

Data Preparation: Preparing data after the import

temp_df = filter(nepal_dt, grepl("tax", tolower(IndicatorName), fixed = TRUE) | grepl("tax", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- temp_df
nepal_df

dim(nepal_df)

[1] 53 66

temp_df = filter(nepal_dt, grepl("gdp", tolower(IndicatorName), fixed = TRUE) | grepl("gdp", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- rbind(nepal_df, temp_df)
nepal_df

dim(nepal_df)

[1] 143  66

temp_df = filter(nepal_dt, grepl("employment", tolower(IndicatorName), fixed = TRUE) | grepl("employment", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- rbind(nepal_df, temp_df)
nepal_df

# Drop first and second column
nepal_df <- nepal_df[-c(1,2)]
nepal_df

# unique(nepal_df$IndicatorName)
#table(tolower(nepal_df$IndicatorName))

# Transposing the dataframe
# df_t <- (t(nepal_df))
df_t <- transpose(nepal_df)
rownames(df_t) <- colnames(nepal_df)
colnames(df_t) <- rownames(nepal_df)
View(df_t)

df_t[0,]

# Rename the columns with the first row. Columns are not properly renamed from above lines.
colnames(df_t) <- df_t[2,]
# Remove the first and second row.
df_t <- df_t[-1:-2,]
nepal_df <- df_t
View(nepal_df)

# Keep rownames as a first column
#setDT(df_t, keep.rownames = TRUE)[]
nepal_df <- cbind(names = rownames(nepal_df), nepal_df)
colnames(nepal_df)[1] <- "YEAR"
# Removing a character 'X' from the column: YEAR in nepal_df
nepal_df$YEAR <- gsub("X","",as.character(nepal_df$YEAR))
nepal_df

dim(nepal_df)[2]

[1] 243

nepal_df

# Converting columns to numeric types
#nepal_df$TM.TAX.MRCH.WM.AR.ZS = as.numeric(as.character(nepal_df$TM.TAX.MRCH.WM.AR.ZS))
#nepal_df$NY.GDP.PETR.RT.ZS = as.numeric(as.character(nepal_df$NY.GDP.PETR.RT.ZS))
nepal_df[1:dim(nepal_df)[2]] <- sapply(nepal_df[1:dim(nepal_df)[2]],as.numeric)
sapply(nepal_df, class)

                    YEAR     TM.TAX.MRCH.WM.AR.ZS        TM.TAX.MRCH.IP.ZS           NY.TAX.NIND.KN 
               "numeric"                "numeric"                "numeric"                "numeric" 
       TM.TAX.TCOM.BC.ZS        TM.TAX.MANF.BC.ZS        GC.TAX.INTT.RV.ZS     TM.TAX.MRCH.WM.FN.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.MRCH.SM.AR.ZS        TM.TAX.TCOM.IP.ZS        TM.TAX.MANF.IP.ZS           IC.TAX.GIFT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GC.TAX.TOTL.GD.ZS        GC.TAX.GSRV.VA.ZS        IC.TAX.LABR.CP.ZS           GC.TAX.YPKG.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
       TM.TAX.MRCH.BR.ZS           NY.TAX.NIND.CN        TM.TAX.MRCH.SR.ZS        IC.TAX.OTHR.CP.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          GC.TAX.YPKG.ZS           GC.TAX.IMPT.ZS           GC.TAX.OTHR.CN           GC.TAX.IMPT.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.TCOM.WM.AR.ZS     TM.TAX.MANF.WM.AR.ZS              IC.TAX.PAYM           GC.TAX.EXPT.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
       IC.TAX.TOTL.CP.ZS           IC.FRM.INFM.ZS           GC.TAX.GSRV.CN           GC.TAX.INTT.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.TCOM.WM.FN.ZS     TM.TAX.MANF.WM.FN.ZS     TM.TAX.MRCH.SM.FN.ZS     TM.TAX.TCOM.SM.AR.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.MANF.SM.AR.ZS           IC.FRM.METG.ZS        GC.TAX.GSRV.RV.ZS        TM.TAX.MRCH.BC.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.TAX.NIND.CD     TM.TAX.TCOM.SM.FN.ZS     TM.TAX.MANF.SM.FN.ZS              IC.TAX.METG 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GC.TAX.YPKG.RV.ZS              IC.TAX.DURS           GC.TAX.TOTL.CN        TM.TAX.TCOM.BR.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       TM.TAX.MANF.BR.ZS        TM.TAX.TCOM.SR.ZS        TM.TAX.MANF.SR.ZS        IC.TAX.PRFT.CP.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          GC.TAX.EXPT.ZS        GC.TAX.OTHR.RV.ZS        TG.VAL.TOTL.GD.ZS           NY.GDP.MKTP.KD 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.COAL.RT.ZS        NY.GDP.PCAP.PP.KD        NY.GDP.MINR.RT.ZS           NY.GDP.MKTP.KN 
               "numeric"                "numeric"                "numeric"                "numeric" 
    NY.GDP.DEFL.KD.ZG.AD           NV.SRV.TOTL.ZS        ER.GDP.FWTL.M3.KD     BX.TRF.PWKR.DT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.GDP.PCAP.EM.KD        SE.XPD.TERT.PC.ZS           NY.GDS.TOTL.ZS        NY.GDP.MKTP.KD.ZG 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.DEFL.KD.ZG        SH.XPD.CHEX.GD.ZS        SE.XPD.PRIM.PC.ZS        NY.GDP.PETR.RT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.MKTP.CD           NE.DAB.TOTL.ZS        SH.XPD.GHED.GD.ZS        SE.XPD.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          PA.NUS.PPPC.RF        NY.GDP.MKTP.PP.KD        NY.GDP.DEFL.ZS.AD           NE.GDI.TOTL.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GC.TAX.TOTL.GD.ZS        FS.AST.DOMS.GD.ZS        FM.AST.PRVT.GD.ZS        EN.ATM.CO2E.KD.GD 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.PCAP.PP.CD        NY.GDP.FRST.RT.ZS           NE.GDI.FTOT.ZS        SE.XPD.SECO.PC.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.MKTP.CN.AD           NV.IND.MANF.ZS           NE.TRD.GNFS.ZS        GC.REV.XGRT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GB.XPD.RSDV.GD.ZS     EG.USE.COMM.GD.PP.KD        GC.NLD.TOTL.GD.ZS        BN.CAB.XOKA.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       BG.GSR.NFSV.GD.ZS           NE.CON.PRVT.ZS        GC.LBL.TOTL.GD.ZS        FS.AST.PRVT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    BM.KLT.DINV.WD.GD.ZS           NY.GDP.PCAP.KD           NY.GDP.FCST.CN        FS.AST.CGOV.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       EN.ATM.CO2E.PP.GD     EG.GDP.PUSE.KO.PP.KD        EG.EGY.PRIM.PP.KD        GC.NFN.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       FM.LBL.BMNY.GD.ZS        NY.GDP.PCAP.KD.ZG           NY.GDP.FCST.KD        NY.GDP.TOTL.RT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.MKTP.CN           NE.RSB.GNFS.ZS        MS.MIL.XPND.GD.ZS        NY.GDP.NGAS.RT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.DISC.CN           NV.IND.TOTL.ZS           NE.GDI.FPRV.ZS        GC.DOD.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       FS.AST.DOMO.GD.ZS     EN.ATM.CO2E.PP.GD.KD     BX.KLT.DINV.WD.GD.ZS           NY.GDP.PCAP.KN 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.FCST.KN           NE.IMP.GNFS.ZS           NY.GNS.ICTR.ZS           NY.GDP.PCAP.CD 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.DISC.KN           NV.AGR.TOTL.ZS        CM.MKT.TRAD.GD.ZS        CM.MKT.LCAP.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
              PA.NUS.PPP        NY.GDP.MKTP.PP.CD           NY.GDP.DEFL.ZS           NE.EXP.GNFS.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.PCAP.CN           NY.GDP.FCST.CD           NE.CON.TOTL.ZS        GC.AST.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       EG.GDP.PUSE.KO.PP           NE.CON.GOVT.ZS        GC.XPN.TOTL.GD.ZS        FD.AST.PRVT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.UEM.NEET.ZS        SL.UEM.1524.FE.ZS           SL.SRV.EMPL.ZS           SL.FAM.WORK.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    SL.EMP.TOTL.SP.FE.ZS        SL.AGR.EMPL.MA.ZS  per_lm_alllm.cov_q5_tot        SL.UEM.INTM.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.TLF.PART.ZS     SL.TLF.0714.WK.MA.ZS        SL.SRV.0714.MA.ZS        SL.FAM.0714.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.SELF.MA.ZS        SL.AGR.0714.FE.ZS  per_lm_alllm.cov_q1_tot        SL.UEM.TOTL.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.1524.MA.ZS        SL.TLF.0714.MA.ZS        SL.IND.EMPL.FE.ZS     SL.EMP.TOTL.SP.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
 SL.EMP.1524.SP.FE.NE.ZS     SL.UEM.TOTL.FE.NE.ZS     SL.UEM.1524.MA.NE.ZS        SL.TLF.0714.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
 SL.EMP.TOTL.SP.MA.NE.ZS           SL.AGR.EMPL.ZS           SL.UEM.INTM.ZS           SL.SRV.0714.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.FAM.0714.ZS           SL.EMP.SELF.ZS        SL.AGR.0714.MA.ZS  per_lm_alllm.cov_q2_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.TOTL.MA.ZS           SL.UEM.1524.ZS     SL.TLF.0714.SW.FE.ZS           SL.IND.EMPL.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.TOTL.SP.ZS  SL.EMP.1524.SP.MA.NE.ZS        SL.UEM.INTM.FE.ZS        SL.TLF.PART.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.SRV.0714.FE.ZS        SL.FAM.0714.FE.ZS        SL.EMP.SELF.FE.ZS per_lm_alllm.cov_pop_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.NEET.MA.ZS     SL.UEM.1524.FE.NE.ZS           SL.TLF.0714.ZS        SL.SRV.EMPL.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.FAM.WORK.MA.ZS  SL.EMP.TOTL.SP.FE.NE.ZS        SL.AGR.EMPL.FE.ZS  per_lm_alllm.cov_q4_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.WAG.0714.MA.ZS        SL.UEM.BASC.FE.ZS        SL.TLF.0714.SW.ZS        SL.SLF.0714.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.WORK.FE.ZS        SL.EMP.MPYR.FE.ZS           SL.WAG.0714.ZS        SL.UEM.BASC.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.SLF.0714.MA.ZS        SL.EMP.WORK.MA.ZS        SL.EMP.MPYR.MA.ZS per_lm_alllm.adq_pop_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.NEET.FE.ZS        SL.TLF.0714.WK.ZS        SL.SRV.EMPL.FE.ZS        SL.FAM.WORK.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.SMGT.FE.ZS           SL.AGR.0714.ZS  per_lm_alllm.cov_q3_tot        SL.UEM.TOTL.NE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.ADVN.FE.ZS        SL.MNF.0714.FE.ZS        SL.EMP.VULN.FE.ZS     SL.EMP.1524.SP.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.UEM.BASC.ZS        SL.TLF.PART.FE.ZS     SL.TLF.0714.WK.FE.ZS           SL.SLF.0714.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.EMP.WORK.ZS           SL.EMP.MPYR.ZS  per_lm_alllm.ben_q1_tot           SL.UEM.TOTL.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.ADVN.MA.ZS     SL.TLF.0714.SW.MA.ZS        SL.MNF.0714.MA.ZS        SL.EMP.VULN.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    SL.EMP.1524.SP.NE.ZS     SL.UEM.TOTL.MA.NE.ZS        SL.UEM.1524.NE.ZS        SL.IND.EMPL.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    SL.EMP.TOTL.SP.NE.ZS     SL.EMP.1524.SP.FE.ZS        SL.WAG.0714.FE.ZS           SL.UEM.ADVN.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.MNF.0714.ZS           SL.EMP.VULN.ZS        SL.EMP.1524.SP.ZS 
               "numeric"                "numeric"                "numeric" 

# Replace NA values with 0
#nepal_df["TM.TAX.MRCH.WM.AR.ZS"][is.na(nepal_df["TM.TAX.MRCH.WM.AR.ZS"])] <- 0
#nepal_df["NY.GDP.PETR.RT.ZS"][is.na(nepal_df["NY.GDP.PETR.RT.ZS"])] <- 0
# Replace na values with 0 using is.na()
nepal_df[is.na(nepal_df)] <- 0

nepal_df

# Viewing the data after preparing it.
View(nepal_df)

Parameter Selection:

## Sample parameters selection to achieve project objective.
# GC.TAX.GSRV.VA.ZS -> Taxes on goods and services
# GC.TAX.GSRV.CN
# GC.TAX.TOTL.GD.ZS -> Tax revenue (% of GDP)
# IC.TAX.LABR.CP.ZS -> Labor tax and contributions (% of commercial profits) | Labor tax and contributions is the amount of taxes and mandatory contributions on labor paid by the business.
# GC.TAX.YPKG.CN -> Taxes on income, profits and capital gains (current LCU)
# GC.TAX.IMPT.ZS -> Customs and other import duties (% of tax revenue)
# GC.TAX.EXPT.CN -> Taxes on exports (current LCU)
# IC.TAX.TOTL.CP.ZS -> Total tax and contribution rate (% of profit)
# NY.GDP.MKTP.KD -> GDP (constant 2015 US$)
# NY.GDP.MKTP.KD.ZG -> GDP growth (annual %)
# SL.IND.EMPL.ZS -> Employment in industry (% of total employment) (modeled ILO estimate)
# SL.IND.EMPL.FE.ZS -> Employment in industry, female (% of female employment) (modeled ILO estimate)
# SL.IND.EMPL.MA.ZS -> Employment in industry, male (% of male employment) (modeled ILO estimate)
# SL.AGR.EMPL.ZS -> Employment in agriculture (% of total employment) (modeled ILO estimate)
# SL.AGR.EMPL.FE.ZS -> Employment in agriculture, female (% of female employment) (modeled ILO estimate)
# SL.AGR.EMPL.MA.ZS -> Employment in agriculture, male (% of male employment) (modeled ILO estimate)

## Sample parameter selection to achieve project objective.
# GC.TAX.GSRV.VA.ZS, NY.GDP.MKTP.KD  0.8481471
# GC.TAX.GSRV.VA.ZS, SL.IND.EMPL.ZS  0.8880489
# GC.TAX.GSRV.VA.ZS, SL.IND.EMPL.FE.ZS 0.8928028
# GC.TAX.GSRV.VA.ZS, SL.IND.EMPL.MA.ZS 0.8939309
# GC.TAX.GSRV.VA.ZS, SL.AGR.EMPL.ZS 0.8268747
# GC.TAX.GSRV.VA.ZS, SL.AGR.EMPL.FE.ZS 0.8333567
# GC.TAX.GSRV.VA.ZS, SL.AGR.EMPL.MA.ZS 0.8062022
# GC.TAX.INTT.RV.ZS, SL.IND.EMPL.ZS 0.727295
# GC.TAX.INTT.RV.ZS, SL.IND.EMPL.FE.ZS 0.7059692
# GC.TAX.INTT.RV.ZS, SL.IND.EMPL.MA.ZS 0.7179946
# GC.TAX.TOTL.GD.ZS, SL.IND.EMPL.ZS 0.893035
# GC.TAX.TOTL.GD.ZS, SL.IND.EMPL.FE.ZS 0.8984195
# GC.TAX.TOTL.GD.ZS, SL.IND.EMPL.MA.ZS 0.8992892
# IC.TAX.LABR.CP.ZS
# GC.TAX.YPKG.CN
# GC.TAX.IMPT.ZS
# GC.TAX.EXPT.CN
# IC.TAX.TOTL.CP.ZS

## Sample parameters selection to achieve project objective.
nepal_df <- select(nepal_df, 'YEAR', 'GC.TAX.GSRV.VA.ZS', 'GC.TAX.GSRV.CN', 'GC.TAX.TOTL.GD.ZS', 'IC.TAX.LABR.CP.ZS', 'GC.TAX.YPKG.CN', 'GC.TAX.IMPT.ZS', 'GC.TAX.EXPT.CN', 'IC.TAX.TOTL.CP.ZS', 'NY.GDP.MKTP.KD', 'NY.GDP.MKTP.KD.ZG', 'SL.IND.EMPL.ZS', 'SL.IND.EMPL.FE.ZS', 'SL.IND.EMPL.MA.ZS', 'SL.AGR.EMPL.ZS', 'SL.AGR.EMPL.FE.ZS', 'SL.AGR.EMPL.MA.ZS')
nepal_df

Data Quality: Checking the data

summary(nepal_df)

      YEAR      GC.TAX.GSRV.VA.ZS GC.TAX.GSRV.CN         GC.TAX.TOTL.GD.ZS IC.TAX.LABR.CP.ZS
 Min.   :1960   Min.   : 0.000    Min.   :           0   Min.   : 0.000    Min.   : 0.000   
 1st Qu.:1975   1st Qu.: 0.000    1st Qu.:           0   1st Qu.: 0.000    1st Qu.: 0.000   
 Median :1990   Median : 3.186    Median :  1743600000   Median : 3.242    Median : 0.000   
 Mean   :1990   Mean   : 4.652    Mean   : 44124665452   Mean   : 5.477    Mean   : 3.113   
 3rd Qu.:2006   3rd Qu.: 7.953    3rd Qu.: 26728675000   3rd Qu.: 8.983    3rd Qu.: 0.000   
 Max.   :2021   Max.   :16.909    Max.   :424000000000   Max.   :19.809    Max.   :26.200   
 GC.TAX.YPKG.CN         GC.TAX.IMPT.ZS   GC.TAX.EXPT.CN       IC.TAX.TOTL.CP.ZS NY.GDP.MKTP.KD       
 Min.   :           0   Min.   : 0.000   Min.   :         0   Min.   : 0.000    Min.   : 3312006963  
 1st Qu.:           0   1st Qu.: 0.000   1st Qu.:         0   1st Qu.: 0.000    1st Qu.: 4680519798  
 Median :   384000000   Median : 8.884   Median :  16100000   Median : 0.000    Median : 8516316247  
 Mean   : 21067623604   Mean   :13.259   Mean   : 212167572   Mean   : 7.863    Mean   :11498688828  
 3rd Qu.:  8722050000   3rd Qu.:26.993   3rd Qu.: 366500000   3rd Qu.: 0.000    3rd Qu.:16270064206  
 Max.   :213000000000   Max.   :36.967   Max.   :1069880000   Max.   :41.800    Max.   :31149050463  
 NY.GDP.MKTP.KD.ZG SL.IND.EMPL.ZS   SL.IND.EMPL.FE.ZS SL.IND.EMPL.MA.ZS SL.AGR.EMPL.ZS  SL.AGR.EMPL.FE.ZS
 Min.   :-2.977    Min.   : 0.000   Min.   :0.000     Min.   : 0.000    Min.   : 0.00   Min.   : 0.00    
 1st Qu.: 2.025    1st Qu.: 0.000   1st Qu.:0.000     1st Qu.: 0.000    1st Qu.: 0.00   1st Qu.: 0.00    
 Median : 4.041    Median : 0.000   Median :0.000     Median : 0.000    Median : 0.00   Median : 0.00    
 Mean   : 3.720    Mean   : 4.974   Mean   :2.703     Mean   : 7.345    Mean   :34.22   Mean   :38.54    
 3rd Qu.: 5.258    3rd Qu.:11.735   3rd Qu.:6.260     3rd Qu.:16.938    3rd Qu.:72.10   3rd Qu.:81.94    
 Max.   : 9.681    Max.   :15.110   Max.   :8.620     Max.   :23.310    Max.   :82.33   Max.   :90.39    
 SL.AGR.EMPL.MA.ZS
 Min.   : 0.00    
 1st Qu.: 0.00    
 Median : 0.00    
 Mean   :29.81    
 3rd Qu.:62.73    
 Max.   :74.79    

Correlation Analysis: Exploring relationship between employment, tax and GDP. Understanding what drives economic activity.

# Finding correlation between each columns in the dataframe
# cor(nepal_df$TM.TAX.MRCH.WM.AR.ZS, nepal_df$NY.GDP.PETR.RT.ZS)
# cor(nepal_df$GC.TAX.TOTL.GD.ZS, nepal_df$SL.IND.EMPL.FE.ZS)
View(cor(nepal_df))

# Correlation matrix plot
corrplot(cor(nepal_df), type="lower")

var(nepal_df$GC.TAX.GSRV.VA.ZS)

[1] 26.21113

# SL.IND.EMPL.ZS  NY.GDP.MKTP.KD

Time series analysis: Trends/patterns in the data over time

# autoregressive integrated moving average (ARIMA) - need to look at it
# GDP = Consumption + Investment + Government spending + Net exports
p <- ggplot(nepal_df, aes(x=nepal_df$YEAR, y=nepal_df$GC.TAX.GSRV.VA.ZS)) +
  geom_line( color="steelblue") + 
  geom_point() +
  xlab("YEAR") +
  ylab("Taxes on goods and services(%)") +
  ggtitle("Percent increase on tax on goods & services each year")
  #scale_x_date(limit=c(as.Date("1960-01-01"),as.Date("2022-12-30"))) +
  
p

# Check tax and gdp over time
coeff <- 10
tax_color <- "black"
gdp_color <- "steelblue"
ggplot(nepal_df, aes(x=nepal_df$YEAR)) +
  
  geom_line( aes(y=nepal_df$GC.TAX.GSRV.CN), size=0.5, color=tax_color) + 
  geom_line( aes(y=nepal_df$NY.GDP.MKTP.KD), size=0.5, color=gdp_color) +
  
  geom_point(aes(y = nepal_df$GC.TAX.GSRV.CN), size=2, color=tax_color) +
  geom_point(aes(y = nepal_df$NY.GDP.MKTP.KD), size=2, color=gdp_color) +
  
  scale_y_continuous(
    
    # First axis
    name = "Taxes on goods and services (current LCU)",
    
    # Second axis
    sec.axis = sec_axis(~.*1, name="GDP (constant 2015 US$)")
  ) +
#  theme_ipsum() +
  scale_x_continuous(
    name = "YEAR"
  ) +
  theme(
    axis.title.y = element_text(color = tax_color, size=13),
    axis.title.y.right = element_text(color = gdp_color, size=13)
  ) +
  ggtitle("Tax and GDP over time") +
  theme(plot.title = element_text(hjust = 0.5)) #Title to be at center

Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.

coeff <- 10

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls

tax_color <- "black"
gdp_color <- "steelblue"
ggplot(nepal_df, aes(x=nepal_df$YEAR)) +
  
  geom_line( aes(y=nepal_df$SL.IND.EMPL.ZS), size=0.5, color=tax_color) + 
  geom_line( aes(y=nepal_df$SL.AGR.EMPL.ZS), size=0.5, color=gdp_color) +
  
  geom_point(aes(y = nepal_df$SL.IND.EMPL.ZS), size=2, color=tax_color) +
  geom_point(aes(y = nepal_df$SL.AGR.EMPL.ZS), size=2, color=gdp_color) +
  
  scale_y_continuous(
    
    # First axis
    name = "Employment in industry (% of total employment)",
    
    # Second axis
    sec.axis = sec_axis(~.*1, name="Employment in agriculture (% of total employment)")
  ) +
#  theme_ipsum() +
  scale_x_continuous(
    name = "YEAR"
  ) +
  theme(
    axis.title.y = element_text(color = tax_color, size=13),
    axis.title.y.right = element_text(color = gdp_color, size=13)
  ) +
  ggtitle("Employment in industry & agriculture over time") +
  theme(plot.title = element_text(hjust = 0.5)) #Title to be at center

ggplot(nepal_df, aes(x = GC.TAX.GSRV.CN, y = NY.GDP.MKTP.KD)) +
  geom_point() +
geom_smooth() + 
# Add a regression line
xlab("Taxes on goods and services (current LCU)") +
ylab("GDP (constant 2015 US$)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: GDP x taxes on goods & services")

# Checking GDP growth on every tax % increase
# with trend line (regression line)
ggplot(nepal_df, aes(x = GC.TAX.GSRV.VA.ZS, y = NY.GDP.MKTP.KD)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Taxes on goods and services (% value added of industry and services)") +
ylab("GDP (constant 2015 US$)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: GDP x taxes on goods & services")

ggplot(nepal_df, aes(x = SL.IND.EMPL.ZS, y = GC.TAX.GSRV.VA.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Employment in industry (% of total employment)") +
ylab("Taxes on goods and services (% value added of industry and services)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Tax on goods & services X Employment in industry")

ggplot(nepal_df, aes(x = GC.TAX.GSRV.VA.ZS, y = SL.AGR.EMPL.ZS )) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Taxes on goods and services (% value added of industry and services)") +
ylab("Employment in agriculture (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Tax on goods & services X Employment in agriculture")

ggplot(nepal_df, aes(x = GC.TAX.IMPT.ZS, y = SL.IND.EMPL.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Customs and other import duties (% of tax revenue)") +
ylab("Employment in industry (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Customs Import duties X Employment in industry")

ggplot(nepal_df, aes(x = GC.TAX.IMPT.ZS, y = SL.AGR.EMPL.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Customs and other import duties (% of tax revenue)") +
ylab("Employment in agriculture (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Customs Import duties X Employment in agriculture")

ggplot(nepal_df, aes(x = GC.TAX.IMPT.ZS, y = SL.IND.EMPL.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Customs and other import duties (% of tax revenue)") +
ylab("Employment in industry (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Customs Import duties X Employment in industry")

nepal_df$GC.TAX.EXPT.CN 

 [1]          0          0          0          0          0          0          0          0          0          0
[11]          0          0          0          0          0          0          0          0          0          0
[21]          0          0          0          0          0          0          0          0          0          0
[31]   32200000   78000000  115000000  141000000  427000000  332000000  150000000  168000000  217000000  378000000
[41]  432000000  493000000  917000000  855600000  527100000  697900000  625284000  698600000  445600000  793800000
[51]  915461000  292395000  861574000  439097000 1069880000  314849716  159554771  125130000  102360000  237634000
[61]  112370000          0

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls
7: In readChar(file, size, TRUE) : truncating string with embedded nuls
8: In readChar(file, size, TRUE) : truncating string with embedded nuls
9: In readChar(file, size, TRUE) : truncating string with embedded nuls
10: In readChar(file, size, TRUE) : truncating string with embedded nuls

#y = GC.TAX.GSRV.VA.ZS

ggplot(nepal_df, aes(x = nepal_df$SL.IND.EMPL.ZS, y = nepal_df$NY.GDP.MKTP.KD, fill = nepal_df$SL.IND.EMPL.ZS)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.08) +
  #theme_bw() +
  xlab("Employment increase(%)") +
  ylab("GDP (constant 2015 US$)") +
  theme(axis.text.x = element_text(size = 10)) +
  theme(axis.text.y = element_text(size = 10)) +
  ggtitle("Bar plot: GDP vs Employment increase(%)")

ggplot(nepal_df, aes(x = nepal_df$GC.TAX.GSRV.VA.ZS, y = nepal_df$NY.GDP.MKTP.KD, fill = nepal_df$GC.TAX.GSRV.VA.ZS)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.08) +
  #theme_bw() +
  xlab("Taxes on goods and services(%)") +
  ylab("GDP (constant 2015 US$)") +
  theme(axis.text.x = element_text(size = 10)) +
  theme(axis.text.y = element_text(size = 10)) +
  ggtitle("Bar plot: GDP vs Taxes on goods & services(%)")

#GC.TAX.GSRV.VA.ZS, NY.GDP.MKTP.KD

#a <- filter(nepal_df, YEAR>2012)
#select(a, GC.TAX.GSRV.CN, NY.GDP.MKTP.KD)

nepal_df$NY.GDP.MKTP.KD

Regression:

# R
help("scale_x_continuous")

Cluster:

# C

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